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Commodity Futures

These entries address investing and trading in commodities and commodity futures as an alternative asset class to equities.

“Real” Assets and Inflation

Which asset class best hedges inflation? In the September 2012 draft of his book chapter entitled “‘Real’ Assets”, Andrew Ang examines the behaviors of the following assets commonly thought to hold their value during times of high inflation (“real” assets): inflation-linked bonds, commodities, real estate and U.S. Treasury bills (T-bill). He focuses on inflation as year-over-year change in the U.S. Consumer Price Index for all urban consumers and all items, but considers also inflation rates for medical care and higher education. He distinguishes inflation hedging (measured by correlation of returns and inflation) from long-run asset class performance. Using asset class proxy returns and U.S. inflation rates as available through 2011, he finds that: Keep Reading

Diversification Power of Commodities

Are commodities effective diversifiers for stocks and bonds? In his September 2012 paper entitled “Commodity Investments: The Missing Piece of the Portfolio Puzzle?”, Xiaowei Kang examines the diversification properties of commodity indexes relative to stock and bond indexes. He focuses on the widely used S&P GSCI, composed of 24 commodities with liquid futures markets weighted by world production value. He also considers the S&P GSCI Dynamic Roll, designed to suppress negative roll returns by rolling into longer-dated (nearby) futures contracts when a commodity’s term structure is in contango (backwardation). Using monthly levels of these indexes, MSCI World (to represent stocks) and Barclays Global Aggregate Bond Index (to represent bonds), along with contemporaneous U.S. Treasury bill yields to calculate excess returns, from as early as December 1970 through June 2012, he finds that: Keep Reading

Managed Futures as Portfolio Diversifier

Are managed futures programs good portfolio diversifiers? In his September 2012 paper entitled “Revisiting Kat’s Managed Futures and Hedge Funds: A Match Made in Heaven”, Thomas Rollinger updates prior research exploring the diversification effects of adding managed futures to traditional portfolios of stocks and bonds and to portfolios including stocks, bonds and hedge funds. His proxies for the four asset classes are: (1) for stocks, the S&P 500 Total Return Index; (2) for bonds, the Barclays U.S. Aggregate Bond Index; (3) for hedge funds, the HFRI Fund Weighted Composite Index; and, (4) for managed futures programs, the Barclay Systematic Traders Index (focused on systematic trend-following strategies). He assumes monthly (frictionless) portfolio rebalancing. Using monthly returns for the four asset class indexes during June 2001 through December 2011, he finds that: Keep Reading

Crude Oil and Natural Gas Prices Reliably Intertwined?

In mid-2008, a reader speculated and asked: “You have probably heard of the historical 6:1 crude oil/natural gas price ratio. This relationship is said to be mean reverting based on the thermal equivalence of the two commodities. Does this ratio have any predictive power for the future prices of oil or natural gas? If there is no predictive power for this ratio, then it could mean that the thermal equivalence itself shifts over time. And hedge funds who are long natural gas right now are making a huge fundamental mistake.” If there are relationships, we hypothesize that a high (low) crude oil-natural gas price ratio should predict future changes in the prices of natural gas of crude oil to decrease (increase) the ratio. Using the monthly composite U.S. refiner cost of crude oil (nominal dollars per barrel) and the monthly U.S. wellhead natural gas price (nominal dollars per thousand cubic feet) for January 1976 through June 2012 (438 months), we find that: Keep Reading

COT Data Predictive for S&P 500 Index?

The zero-sum S&P 500 futures/options market involves three groups of traders: (1) commercial hedgers; (2) non-commercial traders (large speculators); and, (3) non-reportable traders (small or retail speculators) representative of the public. The Commodity Futures Trading Commission (CFTC) collects and publishes aggregate positions (short, long and spread) for each group in a weekly Commitment of Traders (COT) report. CFTC releases reports on Fridays for positions as of the preceding Tuesdays. Are the behaviors of these groups in trading S&P 500 index futures/options reliable indicators of future stock market direction? To investigate, we relate weekly S&P 500 Index futures/options short-long ratios for the three trader categories to S&P 500 Index returns. Using historical weekly COT report data for S&P 500 Index futures and options combined and corresponding weekly dividend-adjusted prices for SPDR S&P 500 (SPY) as a tradable proxy for the index during March 1995 (the earliest available COT data) through early September 2012 (912 weeks), we find that: Keep Reading

Evolution of Commodity Futures Indexes

Does the latest generation of commodity futures indexes, which systematically exploits both backwardation and contango, outperform its predecessors? In her July 2012 paper entitled “Comparing First, Second and Third Generation Commodity Indices”, Joelle Miffre reviews the evolution of commodity futures indexes and assesses the performance of three groups of these indexes: (1) first generation, which are long-only and generally ignore backwardation and contango; (2) second generation, which are also long-only but attempt to mitigate contango while exploiting backwardation; and, (3) third generation, which are long-short to exploit both backwardation and contango. Using monthly levels of 6 first, 23 second and 9 third generation commodity futures indexes from the end of May 2008 through April 2012, she finds that: Keep Reading

Technical Cloning of Hedge Funds with Futures

How effective is technical cloning of hedge funds (attempting to capture a hedge fund’s future returns via a portfolio of liquid assets that empirically replicates the fund’s historical returns)? In the July 2012 version of their paper entitled “Send in the Clones? Hedge Fund Replication Using Futures Contracts”, Nicolas Bollen and Gregg Fisher test whether a replication process can capture some of the benefits of hedge funds (diversification and high Sharpe ratio) while avoiding associated high fees, illiquidity and opacity. They choose one broad and nine strategy-focused hedge fund indexes as targets for replication. They seek to replicate hedge fund index returns with combinations of five fully collateralized futures contracts: U.S. Dollar Index; 10-year T-Note; Gold; Crude Oil; and, S&P 500 Index. Fully collateralized means that they cover potential exposure (positive or negative) with cash earning the risk-free rate (one-month LIBOR). Specifically, they set weights for the futures contracts each month based on linear regression of monthly returns for a hedge fund index versus returns for the five futures contracts over a rolling historical window (see the figure below). They calculate futures contract returns based on holding the nearest-to-expiration contract and rolling to the next maturity five days before expiration. While this process could exploit hedge fund index timing of market factors, it cannot capture any idiosyncratic (non-factor) alpha. Using monthly returns for the ten hedge fund indexes and the five futures contract series during January 1994 through December 2011, they find that: Keep Reading

Commodity Futures Investing Updates

How has recent data meshed with seminal research on commodity futures? In the June 2012 version of their paper entitled “Commodity Investing”, Geert Rouwenhorst and Ke Tang review and update research relevant to investing in commodity futures, with trader positions recorded via Commitments of Traders (COT) reports issued by the Commodity Futures Trading Commission monthly during 1986 through 1992, weekly since 1993 and more granularly since 2006. They assume commercial traders are hedging physical commodities and non-commercial traders are speculators. Using monthly prices and and trader positions for 28 commodity futures contract series during 1986 through 2010, they find that: Keep Reading

Short-term VIX Futures Performance

In general, when the U.S. stock market goes down, the S&P 500 volatility index (VIX) goes up. VIX is not investable, but VIX futures are available. Are short-term VIX futures a good way to hedge equity market declines and guard against market blow-ups? To investigate we focus on returns from holding the contract nearest to maturity, rolling to the next nearest on maturity dates. For simplicity, we assume that rolling is frictionless (favorable to futures) and that available capital always matches a round number of futures contracts (no residual cash). Using daily levels of VIX and daily settlement values of all VIX futures series from late March 2004 through late March 2012 (eight years), we find that: Keep Reading

Enhancing Financial Markets Volatility Prediction

Are there economic and financial variables that meaningfully predict return volatilities of financial markets? In their March 2012 paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of 38 economic and financial variables to predict return volatilities of four asset classes (stocks, foreign exchange, bonds and commodities). Asset class proxies are: (1) the S&P 500 Index; (2) spot levels for a basket of currencies versus the U.S. dollar; (3) 10-year Treasury note futures contract prices; and, (4) the S&P GSCI. They calculate actual (realized) monthly asset class volatilities from daily returns. They construct out-of-sample volatility forecasts based on iterative inception-to-date regressions of volatilities versus predictive variables. They use an autoregressive model (simple realized volatility persistence) as a benchmark. Using monthly data for 13 economic/financial variables and the S&P 500 Index realized volatility over the long period December 1926 through December 2010 (1,009 months) and monthly data for 38 variables and all four asset class volatilities during 1983 through 2010 (366 months), they find that: Keep Reading

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